{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Essential Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "from matplotlib.lines import Line2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n",
    "from sklearn.datasets import load_iris, load_breast_cancer, load_wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_iris_np():\n",
    "    iris = load_iris()\n",
    "    X = MinMaxScaler().fit_transform(iris.data)\n",
    "    y = iris.target\n",
    "    return X, y\n",
    "\n",
    "def load_wine_np():\n",
    "    wine = load_wine()\n",
    "    X = MinMaxScaler().fit_transform(wine.data)\n",
    "    y = wine.target\n",
    "    return X, y\n",
    "\n",
    "def load_breast_cancer_np():\n",
    "    breast_cancer = load_breast_cancer()\n",
    "    X = MinMaxScaler().fit_transform(breast_cancer.data)\n",
    "    y = breast_cancer.target\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to select dataset\n",
    "\n",
    "#X, y = load_iris_np()\n",
    "#X, y = load_wine_np()\n",
    "#X, y = load_breast_cancer_np()\n",
    "#X, y = load_Pendigits_np()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthetic Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_artificial2D(path, skiprows):\n",
    "    full_path = \"./Artificial/\" + path\n",
    "    df = pd.read_csv(full_path, skiprows=skiprows, names=['x', 'y', 'class'])\n",
    "    labels = df[\"class\"].to_numpy()\n",
    "    label_enc = LabelEncoder()\n",
    "    labels = label_enc.fit_transform(labels)\n",
    "    df.drop(columns=[\"class\"], inplace=True)\n",
    "    data = df.to_numpy()\n",
    "    \n",
    "    return data, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_details = {\n",
    "    0:(\"2d-4c-no4.arff\", 10),\n",
    "    1:(\"2d-10c.arff\", 7),\n",
    "    2:(\"aggregation.arff\", 12),\n",
    "    3:(\"D31.arff\", 11),\n",
    "    4:(\"impossible.arff\", 10),\n",
    "    5:(\"R15.arff\", 10),\n",
    "    6:(\"twenty.arff\", 8),\n",
    "}\n",
    "\n",
    "# Select synthetic data set\n",
    "data_id = 5\n",
    "path, skiprows = data_details[data_id]\n",
    "X, y = get_artificial2D(path, skiprows)\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The global k-means++ method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GlobalKMeansPP(BaseEstimator):\n",
    "    def __init__(self, n_clusters=2, n_candidates=25, sampling='batch', max_iter=300, tol=1e-4, verbose=0):\n",
    "        self.n_clusters = n_clusters\n",
    "        self.n_candidates = n_candidates\n",
    "        self.max_iter = max_iter\n",
    "        self.tol = tol\n",
    "        self.sampling = sampling\n",
    "        self.verbose = verbose\n",
    "        \n",
    "        self.cluster_centers_ = dict()\n",
    "        self.cluster_distance_space_ = dict()\n",
    "        self.inertia_ = dict()\n",
    "        self.labels_ = dict()\n",
    "        self.sampled_candidates = dict()\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        kmeans = KMeans(n_clusters=1, init='random', n_init=1, tol=self.tol).fit(X)\n",
    "        self.n_data = X.shape[0]\n",
    "        self.cluster_centers_[1] = kmeans.cluster_centers_\n",
    "        self.cluster_distance_space_[1] = kmeans.transform(X).min(axis=1)\n",
    "        self.labels_[1] = kmeans.labels_\n",
    "        self.inertia_[1] = kmeans.inertia_\n",
    "        \n",
    "        for k in range(2, self.n_clusters+1):\n",
    "            if self.verbose > 0: \n",
    "                print(f'Solving {k}-means')\n",
    "                \n",
    "            centroid_candidates = self._sampling(X, self.cluster_distance_space_[k-1])\n",
    "            self.sampled_candidates[k-1] = centroid_candidates\n",
    "            \n",
    "            self.inertia_[k] = float('inf')\n",
    "            for i, xi in enumerate(centroid_candidates): # TODO parallel\n",
    "                current_centroids = np.vstack((self.cluster_centers_[k-1], xi))\n",
    "                kmeans = KMeans(n_clusters=k, init=current_centroids, n_init=1, tol=self.tol)\n",
    "                kmeans = kmeans.fit(X)\n",
    "                \n",
    "                if kmeans.inertia_ < self.inertia_[k]:\n",
    "                    self.cluster_centers_[k] = kmeans.cluster_centers_\n",
    "                    self.labels_[k] = kmeans.labels_\n",
    "                    self.inertia_[k] = kmeans.inertia_\n",
    "                    self.cluster_distance_space_[k] = kmeans.transform(X).min(axis=1)            \n",
    "        return self\n",
    "      \n",
    "    def predict(self, X):\n",
    "        return self.labels_, self.cluster_centers_, self.inertia_\n",
    "    \n",
    "    def transform(self, X):\n",
    "        return self.cluster_distance_space_\n",
    "    \n",
    "    def _sampling(self, X, cluster_distance_space):\n",
    "        if self.sampling == 'batch':\n",
    "            _, selected_indexes = self._kmeans_pp_batch(X, cluster_distance_space)\n",
    "            return X[selected_indexes]\n",
    "        if self.sampling == 'sequential':\n",
    "            _, selected_indexes = self._kmeans_pp_sequential(X, cluster_distance_space)\n",
    "            return X[selected_indexes]\n",
    "        elif self.sampling == 'global':\n",
    "            return X\n",
    "        else:\n",
    "            raise ValueError(\"Wrong sampling method! options = ['batch', 'sequential', 'global']\")\n",
    "    \n",
    "    def _kmeans_pp_batch(self, X, cluster_distance_space):\n",
    "        cluster_distance_space = np.power(cluster_distance_space, 2).flatten()\n",
    "        sum_distance = np.sum(cluster_distance_space)\n",
    "        selection_prob = cluster_distance_space / sum_distance\n",
    "        selected_indexes = np.random.choice(self.n_data, size=self.n_candidates, p=selection_prob, replace=False)\n",
    "        kmeans_pp_selected_centroids = X[selected_indexes]\n",
    "        return kmeans_pp_selected_centroids, selected_indexes\n",
    "    \n",
    "    def _kmeans_pp_sequential(self, X, cluster_distance_space):\n",
    "        selected_indexes = np.zeros(shape=(self.n_candidates), dtype=np.int32)       \n",
    "        cluster_distance_space = cluster_distance_space.flatten()\n",
    "        for i in range(self.n_candidates):\n",
    "            cluster_distance_space_squared = np.power(cluster_distance_space, 2)\n",
    "            sum_distance = np.sum(cluster_distance_space_squared)\n",
    "            selection_prob = cluster_distance_space_squared / sum_distance\n",
    "            selected_indexes[i] = np.random.choice(self.n_data, size=1, p=selection_prob, replace=False)\n",
    "            \n",
    "            candidate = X[selected_indexes[i]]\n",
    "            candidate = np.reshape(candidate, newshape=(1, candidate.shape[0]))\n",
    "            candidate_data_distances = pairwise_distances(candidate, X).flatten()\n",
    "\n",
    "            # Update probability distribution\n",
    "            for i, _ in enumerate(cluster_distance_space):\n",
    "                if candidate_data_distances[i] < cluster_distance_space[i]:\n",
    "                    cluster_distance_space[i] = candidate_data_distances[i]\n",
    "                \n",
    "        kmeans_pp_selected_centroids = X[selected_indexes]\n",
    "        return kmeans_pp_selected_centroids, selected_indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global $k$-means++ example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select number of clusters K\n",
    "n_clusters = 16\n",
    "\n",
    "# Select number of candidates L \n",
    "n_candidates = 15\n",
    "\n",
    "method = {0: 'batch', 1: 'sequential', 2: 'global'}\n",
    "\n",
    "# option=0 selects the k-means++ with batch sampling\n",
    "# option=1 selects the k-means++ with sequential sampling\n",
    "# option=2 selects the global k-means\n",
    "option = 0\n",
    "\n",
    "# Same as in sklearn k-means method\n",
    "tol = 1e-4\n",
    "verbose = 1\n",
    "\n",
    "global_kmeans_pp = GlobalKMeansPP(n_clusters=n_clusters, n_candidates=n_candidates, sampling=method[option], tol=tol, verbose=verbose)\n",
    "global_kmeans_pp.fit(X)\n",
    "clusters, centroids, error = global_kmeans_pp.predict(X)\n",
    "candidates = global_kmeans_pp.sampled_candidates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot $k$-th clustering solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_list = list(mcolors.CSS4_COLORS.keys()) + list(mcolors.XKCD_COLORS.keys())\n",
    "np.random.shuffle(color_list)\n",
    "color_list = ['royalblue', 'gold', 'deepskyblue', 'hotpink', 'mediumorchid', 'crimson', 'salmon', 'limegreen'] + color_list\n",
    "\n",
    "def plot_solution(X, centroids, kmeans_labels, kmeans_centers, candidates):\n",
    "    # Figure\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "    plt.title('{}-means solution'.format(centroids), fontsize=15)\n",
    "    \n",
    "    for axis in ['top','bottom','left','right']:\n",
    "        ax.spines[axis].set_linewidth(2.0)\n",
    "    \n",
    "    # Data scatters\n",
    "    ax.scatter(X[:,0], X[:,1], edgecolors='none', c=[color_list[kmeans_labels[i]] for i in range(kmeans_labels.shape[0])],  s=20)\n",
    "    ax.scatter(candidates[:,0], candidates[:,1], c='lime', marker='P', s=200, edgecolors='black')\n",
    "    ax.scatter(kmeans_centers[:,0], kmeans_centers[:,1], c='r', marker='*', s=500, edgecolors='black')\n",
    "\n",
    "    # Legend\n",
    "    leg_data = Line2D([0], [0], marker='o', color='w', markerfacecolor='black', label='Datapoint', markeredgecolor='black', markersize=10)\n",
    "    leg_centers = Line2D([0], [0], marker='*', color='w', markerfacecolor='r', label='Center', markeredgecolor='black', markersize=20)\n",
    "    leg_candidates = Line2D([0], [0], marker='P', color='w', markerfacecolor='lime', label='Candidate', markeredgecolor='black', markersize=15)\n",
    "    legend = ax.legend(handles=[leg_data, leg_centers, leg_candidates],loc='upper left', fontsize=15)\n",
    "    \n",
    "    #legend = ax.legend(['Data', 'Centers', 'Candidates'], loc='upper right', fontsize=13)\n",
    "    legend.get_frame().set_linewidth(2.0)\n",
    "    legend.get_frame().set_edgecolor('black')\n",
    "    legend.legendHandles[0].set_color('w')\n",
    "    \n",
    "    # Axes\n",
    "    #ax.xlim([-0.2, 1.2])\n",
    "    #ax.ylim([-0.2, 1.2])\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    \n",
    "    # plt.show()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select k to plot solution which clustering solution you want to visualize\n",
    "k = 1\n",
    "plot_solution(X, \"{:d}\".format(k), clusters[k], centroids[k], candidates[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
